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2 Machine Graphics and Vision

2 2019

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Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network

Kurek Jarosław, Antoniuk Izabella, Górski Jarosław, Jegorowa Albina, Świderski Bartosz, Kruk Michał, Wieczorek Grzegorz, Pach Jakub, Orłowski Arkadiusz, Aleksiejuk-Gawron Joanna

Machine Graphics and Vision

2019

Vol. 28, No. 1/4

3--12

This paper presents an improved method for recognizing the drill state on the basisof hole images drilled in a laminated chipboard, using convolutional neural network (CNN) and data augmentation techniques. Three classes were used to describe the drill state: red - for drill that is worn out and should be replaced, yellow - for state in which the system should send a warning to the operator, indicating that this element should be checked manually, and green - denoting the drill that is still in good condition, which allows for further use in the production process. The presented method combines the advantages of transfer learning and data augmentation methods to improve the accuracy of the received evaluations. In contrast to the classical deep learning methods, transfer learning requires much smaller training data sets to achieve acceptable results. At the same time, data augmentation customized for drill wear recognition makes it possible to expand the original dataset and to improve the overall accuracy. The experiments performed have confirmed the suitability of the presented approach to accurate class recognition in the given problem, even while using a small original dataset.

Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network

Kurek Jarosław, Antoniuk Izabella, Górski Jarosław, Jegorowa Albina, Świderski Bartosz, Kruk Michał, Wieczorek Grzegorz, Pach Jakub, Orłowski Arkadiusz, Aleksiejuk-Gawron Joanna

Machine Graphics and Vision

2019

Vol. 28, No. 1/4

13--23

In this paper we introduce the enhanced drill wear recognition method, based on classifiers ensemble, obtained using transfer learning and data augmentation methods. Red, green and yellow classes are used to describe the current drill state. The first one corresponds to the case when drill should be immediately replaced. The second one denotes a tool that is still in a good condition. The final class refers to the case when a drill is suspected of being worn out, and a human expert evaluation would be required. The proposed algorithm uses three different, pretrained network models and adjusts them to the drill wear classification problem. To ensure satisfactory results, each of the methods used was required to achieve accuracy above 90% for the given classification task. Final evaluation is achieved by voting of all three classifiers. Since the initial data set was small (242 instances), the data augmentation method was used to artificially increase the total number of drill hole images. The experiments performed confirmed that the presented approach can achieve high accuracy, even with such a limited set of training data.